Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies
a progressive section in deep learning. Using generative modeling, the underlying …

As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires
large-scale GPU-clusters and a vast number of training images. In this paper, we study the …

Abstract For Generative Adversarial Networks which map a latent distribution to the target
distribution, in this paper, we study how the sampling in latent space can affect the …

With the prevalence of deep learning and convolutional neural network (CNN), data
augmentation is widely used for enriching training samples to gain model training …

Due to the outstanding capability for data generation, Generative Adversarial Networks
(GANs) have attracted considerable attention in unsupervised learning. However, training …

We consider strongly-convex-strongly-concave saddle point problems assuming we have
access to unbiased stochastic estimates of the gradients. We propose a stochastic …

Due to the outstanding capability for data generation, Generative Adversarial Networks
(GANs) have attracted considerable attention in unsupervised learning. However, training …

Deep generative models including generative adversarial networks (GANs) are powerful
unsupervised tools in learning the distributions of data sets. Building a simple GAN …